Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. MethodsIn this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2•5% (95% CI 1•4-4•3) for patients with phenotype A, 30•5% (28•5-32•6) for patients with phenotype B, and 60•7% (53•7-67•2) for patients with phenotype C (log-rank test p<0•0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5•3% [95% CI 3•4-8•1] for phenotype A, 31•3% [28•5-34•2] for phenotype B, and 59•5% [48•8-69•3] for phenotype C; external validation cohort: 3•7% [2•0-6•4] for phenotype A, 23•7% [21•8-25•7] for phenotype B, and 51•4% [41•9-60•7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three...
Right heart catheterization remains necessary for the diagnosis of pulmonary hypertension and, therefore, for the prognostic evaluation of patients with chronic heart failure. The non-invaSive Assessment of Pulmonary vasculoPathy in Heart failure (SAPPHIRE) study was designed to assess the feasibility and prognostic relevance of a non-invasive evaluation of the pulmonary artery vasculature in patients with heart failure and pulmonary hypertension. Patients will undergo a right heart catheterization, cardiac resonance imaging, and a pulmonary function test in order to identify structural and functional parameters allowing the identification of combined pre- and postcapillary pulmonary hypertension, and correlate these findings with the hemodynamic data.
Background Previous parameters such as peak VO2, VE/VCO2 slope and OUES have been described to be prognostic in heart failure (HF). The aim of this study was to identify further prognostic factors of cardiopulmonary exercise testing (CPET) in HF patients. Methods A retrospective analysis of HF patients who underwent CPET from January to November 2019 in a single centre was performed. PETCO2 gradient was defined by the difference between final PETCO2 and baseline PETCO2. HF events were defined as decompensated HF requiring hospital admission or IV diuretics, or decompensated HF resulting in death. Results A total of 64 HF patients were assessed by CPET, HF events occurred in 8 (12.5%) patients. Baseline characteristics are shown in table 1. Patients having HF events had a negative PETCO2 gradient while patients not having events showed a positive PETCO2 gradient (−1.5 [IQR −4.8, 2.3] vs 3 [IQR 1, 5] mmHg; p=0.004). A multivariate Cox proportional-hazards regression analysis revealed that PETCO2 gradient was an independent predictor of HF events (HR 0.74, 95% CI [0.61–0.89]; p=0.002). Kaplan-Meier curves showed a significantly higher incidence of HF events in patients having negative gradients, p=0.002 (figure 1). Conclusion PETCO2 gradient was demonstrated to be a prognostic parameter of CPET in HF patients in our study. Patients having negative gradients had worse outcomes by having more HF events. Time to first event, decompensated heart Funding Acknowledgement Type of funding source: None
Introduction The role of cardiopulmonary exercise testing (CPET) is unquestionable to assess prognosis in heart failure. In patients with valvular heart disease (VHD), the functional capacity (FC) is crucial to aid in the right timing of surgery. The aim of this study was to compare the assessment of the FC by CPET and NYHA and the correlation between ventilatory efficiency parameters and resting systolic pulmonary artery pressure (SPAP). Methods We studied 100 VHD patients (57% female) who underwent a CPET. We calculated the real METS (RM) as indexed peak VO2/3.5 (1 MET=3.5 ml O2/kg/min) and compared to estimated METS (EM) derived by the time of exercise. An agreement analysis between RM, EM and NYHA was calculated. The correlation among VE/Vslope CO2, EqCO2at anaerobic threshold (AT), PETCO2, partial pressure end-tidal CO2 at AT and SPAP was analyzed. Results The results are shown in Table and Figure. The RM and the EM were 4.7±1.7 and 5.5±3, respectively (p<0.01) with a low agreement (ICC=0.6, p<0.01). The agreement between NYHA and the classification obtained from peak % of predicted peak VO2 was very low (weighted kappa =0.06, p=0.28). In patients with severe mitral VHD, the ventilatory efficiency parameters were correlated with SPAP (PETCO2 (AT), r=−0.7, p=0.002; EqCO2 (AT), r=0.5, p=0.04:VE/Vslope CO2, r=0.3, p 0.2), whereas in those with severe aortic VHD, these correlations were much lower (PETCO2 (AT), r=−0.3, p=0.13; EqCO2 (AT), r=0.2, p=0.15; VE/Vslope CO2, r=0.18, p 0.31). Total (n=100) Mitral regurgitation (n=35) Aortic regurgitation (n=23) Age 65 (29–86) 66 (30–84) 65 (11–87) LVEF (%) 62±6 63±6 61±7 SPAP (mmHg) 40±11 39±11 36±8 NYHA I (60%), II (33%), III (7%) I (63%), II (29%), III (9%) I (63%), II (33%), III (4%) Indexed peak VO2 (ml/min/kg) 16±6 17±6 19±8 Peak % predicted VO2 73±18 74±17 79±18 Predicted VO2 AT (%) 58±19 54±19 61±22 Eq CO2 AT 33±6 32±7 32±5 VE/VSlope CO2 33±6 32±7 33±8 PetCO2 AT 34±4 36±4 36±5 Type and degree of VHD Conclusions NYHA scale and estimation of METS derived from the time of exercise clearly overestimated the FC of our population. In our series, the ventilatory inefficiency in patients with mitral VHD could be a surrogate marker of advanced disease and could lead to an earlier intervention.
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